Overview

Dataset statistics

Number of variables14
Number of observations202
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.2 KiB
Average record size in memory112.6 B

Variable types

Numeric11
Categorical3

Alerts

year has constant value "2012.0"Constant
Unnamed: 0 is highly overall correlated with monthHigh correlation
day is highly overall correlated with BUIHigh correlation
RH is highly overall correlated with FFMC and 2 other fieldsHigh correlation
Rain is highly overall correlated with FFMC and 5 other fieldsHigh correlation
FFMC is highly overall correlated with RH and 7 other fieldsHigh correlation
DMC is highly overall correlated with Rain and 6 other fieldsHigh correlation
DC is highly overall correlated with Rain and 6 other fieldsHigh correlation
ISI is highly overall correlated with RH and 7 other fieldsHigh correlation
BUI is highly overall correlated with day and 7 other fieldsHigh correlation
FWI is highly overall correlated with RH and 7 other fieldsHigh correlation
month is highly overall correlated with Unnamed: 0High correlation
Classes is highly overall correlated with FFMC and 5 other fieldsHigh correlation
Unnamed: 0 has unique valuesUnique
Rain has 114 (56.4%) zerosZeros
FWI has 6 (3.0%) zerosZeros

Reproduction

Analysis started2023-09-16 12:48:31.103288
Analysis finished2023-09-16 12:48:53.096356
Duration21.99 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct202
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.76733
Minimum0
Maximum245
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:53.514528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.05
Q158.25
median120.5
Q3177.75
95-th percentile231.95
Maximum245
Range245
Interquartile range (IQR)119.5

Descriptive statistics

Standard deviation70.781075
Coefficient of variation (CV)0.58609457
Kurtosis-1.1841777
Mean120.76733
Median Absolute Deviation (MAD)61
Skewness0.045411372
Sum24395
Variance5009.9605
MonotonicityNot monotonic
2023-09-16T18:18:53.762775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164 1
 
0.5%
82 1
 
0.5%
157 1
 
0.5%
168 1
 
0.5%
5 1
 
0.5%
114 1
 
0.5%
55 1
 
0.5%
145 1
 
0.5%
222 1
 
0.5%
202 1
 
0.5%
Other values (192) 192
95.0%
ValueCountFrequency (%)
0 1
0.5%
1 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
12 1
0.5%
ValueCountFrequency (%)
245 1
0.5%
244 1
0.5%
242 1
0.5%
241 1
0.5%
240 1
0.5%
238 1
0.5%
237 1
0.5%
236 1
0.5%
235 1
0.5%
234 1
0.5%

day
Real number (ℝ)

Distinct31
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.851485
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:53.961998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29.95
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.6850774
Coefficient of variation (CV)0.54790307
Kurtosis-1.1518508
Mean15.851485
Median Absolute Deviation (MAD)7
Skewness-1.4932782 × 10-6
Sum3202
Variance75.43057
MonotonicityNot monotonic
2023-09-16T18:18:54.180745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 8
 
4.0%
23 8
 
4.0%
20 8
 
4.0%
15 8
 
4.0%
22 8
 
4.0%
6 8
 
4.0%
17 8
 
4.0%
9 8
 
4.0%
30 8
 
4.0%
11 7
 
3.5%
Other values (21) 123
60.9%
ValueCountFrequency (%)
1 7
3.5%
2 6
3.0%
3 4
2.0%
4 6
3.0%
5 7
3.5%
6 8
4.0%
7 6
3.0%
8 6
3.0%
9 8
4.0%
10 6
3.0%
ValueCountFrequency (%)
31 3
 
1.5%
30 8
4.0%
29 7
3.5%
28 4
2.0%
27 6
3.0%
26 6
3.0%
25 7
3.5%
24 7
3.5%
23 8
4.0%
22 8
4.0%

month
Categorical

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
7.0
55 
6.0
52 
9.0
50 
8.0
45 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters606
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.0
2nd row9.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
7.0 55
27.2%
6.0 52
25.7%
9.0 50
24.8%
8.0 45
22.3%

Length

2023-09-16T18:18:54.372575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T18:18:54.569059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7.0 55
27.2%
6.0 52
25.7%
9.0 50
24.8%
8.0 45
22.3%

Most occurring characters

ValueCountFrequency (%)
. 202
33.3%
0 202
33.3%
7 55
 
9.1%
6 52
 
8.6%
9 50
 
8.3%
8 45
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 404
66.7%
Other Punctuation 202
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 202
50.0%
7 55
 
13.6%
6 52
 
12.9%
9 50
 
12.4%
8 45
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 606
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 202
33.3%
0 202
33.3%
7 55
 
9.1%
6 52
 
8.6%
9 50
 
8.3%
8 45
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 202
33.3%
0 202
33.3%
7 55
 
9.1%
6 52
 
8.6%
9 50
 
8.3%
8 45
 
7.4%

year
Categorical

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2012.0
202 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1212
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012.0
2nd row2012.0
3rd row2012.0
4th row2012.0
5th row2012.0

Common Values

ValueCountFrequency (%)
2012.0 202
100.0%

Length

2023-09-16T18:18:54.761518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T18:18:54.930370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012.0 202
100.0%

Most occurring characters

ValueCountFrequency (%)
2 404
33.3%
0 404
33.3%
1 202
16.7%
. 202
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1010
83.3%
Other Punctuation 202
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 404
40.0%
0 404
40.0%
1 202
20.0%
Other Punctuation
ValueCountFrequency (%)
. 202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1212
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 404
33.3%
0 404
33.3%
1 202
16.7%
. 202
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 404
33.3%
0 404
33.3%
1 202
16.7%
. 202
16.7%

RH
Real number (ℝ)

Distinct58
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.381188
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:55.087219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37.05
Q153
median64
Q373
95-th percentile85.9
Maximum90
Range69
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.338073
Coefficient of variation (CV)0.22984611
Kurtosis-0.30607578
Mean62.381188
Median Absolute Deviation (MAD)10
Skewness-0.28010955
Sum12601
Variance205.58034
MonotonicityNot monotonic
2023-09-16T18:18:55.337840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 9
 
4.5%
54 7
 
3.5%
65 7
 
3.5%
55 7
 
3.5%
58 6
 
3.0%
66 6
 
3.0%
68 6
 
3.0%
80 6
 
3.0%
73 6
 
3.0%
59 6
 
3.0%
Other values (48) 136
67.3%
ValueCountFrequency (%)
21 1
 
0.5%
24 1
 
0.5%
26 1
 
0.5%
31 1
 
0.5%
34 3
1.5%
35 1
 
0.5%
36 1
 
0.5%
37 2
1.0%
38 1
 
0.5%
41 2
1.0%
ValueCountFrequency (%)
90 1
 
0.5%
89 2
 
1.0%
88 3
1.5%
87 3
1.5%
86 2
 
1.0%
84 2
 
1.0%
83 1
 
0.5%
82 1
 
0.5%
81 5
2.5%
80 6
3.0%

Ws
Real number (ℝ)

Distinct14
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.455446
Minimum8
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:55.554454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12
Q114
median15
Q317
95-th percentile19
Maximum22
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4230434
Coefficient of variation (CV)0.15677603
Kurtosis0.21050605
Mean15.455446
Median Absolute Deviation (MAD)2
Skewness0.077179306
Sum3122
Variance5.8711394
MonotonicityNot monotonic
2023-09-16T18:18:55.743411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
15 36
17.8%
14 35
17.3%
13 25
12.4%
16 24
11.9%
17 24
11.9%
18 22
10.9%
19 13
 
6.4%
21 6
 
3.0%
11 6
 
3.0%
12 6
 
3.0%
Other values (4) 5
 
2.5%
ValueCountFrequency (%)
8 1
 
0.5%
9 2
 
1.0%
11 6
 
3.0%
12 6
 
3.0%
13 25
12.4%
14 35
17.3%
15 36
17.8%
16 24
11.9%
17 24
11.9%
18 22
10.9%
ValueCountFrequency (%)
22 1
 
0.5%
21 6
 
3.0%
20 1
 
0.5%
19 13
 
6.4%
18 22
10.9%
17 24
11.9%
16 24
11.9%
15 36
17.8%
14 35
17.3%
13 25
12.4%

Rain
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52178218
Minimum0
Maximum6.5
Zeros114
Zeros (%)56.4%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:55.931184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.4
95-th percentile3.1
Maximum6.5
Range6.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation1.1570768
Coefficient of variation (CV)2.2175475
Kurtosis10.112257
Mean0.52178218
Median Absolute Deviation (MAD)0
Skewness3.1324887
Sum105.4
Variance1.3388267
MonotonicityNot monotonic
2023-09-16T18:18:56.150794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 114
56.4%
0.1 15
 
7.4%
0.3 9
 
4.5%
0.2 8
 
4.0%
0.4 7
 
3.5%
0.6 6
 
3.0%
0.7 5
 
2.5%
0.5 3
 
1.5%
1.8 3
 
1.5%
1.2 3
 
1.5%
Other values (22) 29
 
14.4%
ValueCountFrequency (%)
0 114
56.4%
0.1 15
 
7.4%
0.2 8
 
4.0%
0.3 9
 
4.5%
0.4 7
 
3.5%
0.5 3
 
1.5%
0.6 6
 
3.0%
0.7 5
 
2.5%
0.8 2
 
1.0%
0.9 1
 
0.5%
ValueCountFrequency (%)
6.5 1
0.5%
6 1
0.5%
5.8 1
0.5%
4.7 1
0.5%
4.6 1
0.5%
4.5 1
0.5%
4.4 1
0.5%
4.1 1
0.5%
4 1
0.5%
3.8 1
0.5%

FFMC
Real number (ℝ)

Distinct150
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.379208
Minimum36.1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:56.383227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.1
5-th percentile48.735
Q172.9
median82.95
Q387.95
95-th percentile92.08
Maximum96
Range59.9
Interquartile range (IQR)15.05

Descriptive statistics

Standard deviation13.203654
Coefficient of variation (CV)0.16845863
Kurtosis1.090112
Mean78.379208
Median Absolute Deviation (MAD)5.95
Skewness-1.3184513
Sum15832.6
Variance174.33648
MonotonicityNot monotonic
2023-09-16T18:18:56.601409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.9 6
 
3.0%
89.4 4
 
2.0%
87 3
 
1.5%
89.1 3
 
1.5%
79.9 3
 
1.5%
88.1 3
 
1.5%
85.4 3
 
1.5%
86.6 2
 
1.0%
86 2
 
1.0%
89 2
 
1.0%
Other values (140) 171
84.7%
ValueCountFrequency (%)
36.1 1
0.5%
37.3 1
0.5%
37.9 1
0.5%
40.9 1
0.5%
41.1 1
0.5%
44.9 1
0.5%
45 1
0.5%
47.4 2
1.0%
48.6 1
0.5%
48.7 1
0.5%
ValueCountFrequency (%)
96 1
0.5%
94.3 1
0.5%
94.2 1
0.5%
93.9 1
0.5%
93.8 1
0.5%
93.3 1
0.5%
93 1
0.5%
92.5 2
1.0%
92.2 1
0.5%
92.1 1
0.5%

DMC
Real number (ℝ)

Distinct144
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.980198
Minimum0.9
Maximum51.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:56.834316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.21
Q15.85
median11.15
Q319.475
95-th percentile36.85
Maximum51.3
Range50.4
Interquartile range (IQR)13.625

Descriptive statistics

Standard deviation10.75316
Coefficient of variation (CV)0.7691708
Kurtosis1.1291386
Mean13.980198
Median Absolute Deviation (MAD)6.7
Skewness1.2048351
Sum2824
Variance115.63045
MonotonicityNot monotonic
2023-09-16T18:18:57.078971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9 5
 
2.5%
7 3
 
1.5%
3.2 3
 
1.5%
9.7 3
 
1.5%
3.4 3
 
1.5%
12.5 3
 
1.5%
6 3
 
1.5%
8.3 3
 
1.5%
3 3
 
1.5%
16 3
 
1.5%
Other values (134) 170
84.2%
ValueCountFrequency (%)
0.9 2
1.0%
1.1 1
 
0.5%
1.3 1
 
0.5%
1.7 1
 
0.5%
1.9 3
1.5%
2.1 1
 
0.5%
2.2 2
1.0%
2.4 1
 
0.5%
2.5 2
1.0%
2.6 2
1.0%
ValueCountFrequency (%)
51.3 1
0.5%
46.6 1
0.5%
46.1 1
0.5%
45.6 1
0.5%
44.2 1
0.5%
43.9 1
0.5%
41.1 1
0.5%
40.5 1
0.5%
37.6 1
0.5%
37 1
0.5%

DC
Real number (ℝ)

Distinct166
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.065347
Minimum7.3
Maximum190.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:57.301806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.705
Q115.2
median33.25
Q370.05
95-th percentile147.6
Maximum190.6
Range183.3
Interquartile range (IQR)54.85

Descriptive statistics

Standard deviation43.944619
Coefficient of variation (CV)0.91426822
Kurtosis1.0888814
Mean48.065347
Median Absolute Deviation (MAD)23.65
Skewness1.3343823
Sum9709.2
Variance1931.1295
MonotonicityNot monotonic
2023-09-16T18:18:57.536178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.2 4
 
2.0%
8 4
 
2.0%
7.8 4
 
2.0%
7.6 4
 
2.0%
8.4 4
 
2.0%
17 3
 
1.5%
8.3 3
 
1.5%
34.5 2
 
1.0%
33.1 2
 
1.0%
15.2 2
 
1.0%
Other values (156) 170
84.2%
ValueCountFrequency (%)
7.3 2
1.0%
7.4 1
 
0.5%
7.5 2
1.0%
7.6 4
2.0%
7.7 2
1.0%
7.8 4
2.0%
8 4
2.0%
8.2 4
2.0%
8.3 3
1.5%
8.4 4
2.0%
ValueCountFrequency (%)
190.6 1
0.5%
181.3 1
0.5%
180.4 1
0.5%
171.3 1
0.5%
168.2 1
0.5%
166 1
0.5%
161.5 1
0.5%
159.1 1
0.5%
151.3 1
0.5%
149.2 1
0.5%

ISI
Real number (ℝ)

Distinct94
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5717822
Minimum0
Maximum16.6
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:57.782909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11.5
median3.3
Q36.9
95-th percentile12.175
Maximum16.6
Range16.6
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation3.8261662
Coefficient of variation (CV)0.83690913
Kurtosis0.66911071
Mean4.5717822
Median Absolute Deviation (MAD)2.2
Skewness1.0904015
Sum923.5
Variance14.639548
MonotonicityNot monotonic
2023-09-16T18:18:58.002095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 8
 
4.0%
1.2 6
 
3.0%
2.8 5
 
2.5%
5.6 5
 
2.5%
1 5
 
2.5%
4.7 5
 
2.5%
1.4 4
 
2.0%
2.2 4
 
2.0%
2.4 4
 
2.0%
5.2 4
 
2.0%
Other values (84) 152
75.2%
ValueCountFrequency (%)
0 2
1.0%
0.1 3
1.5%
0.2 3
1.5%
0.3 2
1.0%
0.4 3
1.5%
0.5 2
1.0%
0.6 2
1.0%
0.7 3
1.5%
0.8 3
1.5%
0.9 1
 
0.5%
ValueCountFrequency (%)
16.6 1
0.5%
16 1
0.5%
15.7 2
1.0%
15.5 1
0.5%
14.2 1
0.5%
13.8 2
1.0%
13.4 1
0.5%
13.2 1
0.5%
12.2 1
0.5%
11.7 1
0.5%

BUI
Real number (ℝ)

Distinct150
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16
Minimum1.4
Maximum57.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:58.237994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.8
Q16.125
median12.25
Q322.4
95-th percentile41.295
Maximum57.1
Range55.7
Interquartile range (IQR)16.275

Descriptive statistics

Standard deviation12.49102
Coefficient of variation (CV)0.78068873
Kurtosis0.88112367
Mean16
Median Absolute Deviation (MAD)7.15
Skewness1.1677402
Sum3232
Variance156.02557
MonotonicityNot monotonic
2023-09-16T18:18:58.455461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1 4
 
2.0%
3 4
 
2.0%
10.9 3
 
1.5%
11.5 3
 
1.5%
14.1 3
 
1.5%
7.7 3
 
1.5%
22.4 3
 
1.5%
2.9 3
 
1.5%
3.9 3
 
1.5%
3.7 2
 
1.0%
Other values (140) 171
84.7%
ValueCountFrequency (%)
1.4 2
1.0%
1.6 1
 
0.5%
1.8 1
 
0.5%
2.2 1
 
0.5%
2.4 2
1.0%
2.6 2
1.0%
2.7 1
 
0.5%
2.8 2
1.0%
2.9 3
1.5%
3 4
2.0%
ValueCountFrequency (%)
57.1 1
0.5%
54.9 1
0.5%
54.7 1
0.5%
50.9 1
0.5%
50.2 1
0.5%
47.5 1
0.5%
46.5 1
0.5%
45.5 1
0.5%
44 1
0.5%
43.1 1
0.5%

FWI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6519802
Minimum0
Maximum26.9
Zeros6
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-09-16T18:18:58.700390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.8
median4.2
Q310.6
95-th percentile20.395
Maximum26.9
Range26.9
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.758891
Coefficient of variation (CV)1.016072
Kurtosis0.17420835
Mean6.6519802
Median Absolute Deviation (MAD)3.75
Skewness1.0300462
Sum1343.7
Variance45.682608
MonotonicityNot monotonic
2023-09-16T18:18:58.923730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 10
 
5.0%
0.8 8
 
4.0%
0.5 8
 
4.0%
0.3 7
 
3.5%
0.9 7
 
3.5%
0 6
 
3.0%
0.1 6
 
3.0%
0.7 4
 
2.0%
0.2 4
 
2.0%
4.2 3
 
1.5%
Other values (102) 139
68.8%
ValueCountFrequency (%)
0 6
3.0%
0.1 6
3.0%
0.2 4
 
2.0%
0.3 7
3.5%
0.4 10
5.0%
0.5 8
4.0%
0.6 3
 
1.5%
0.7 4
 
2.0%
0.8 8
4.0%
0.9 7
3.5%
ValueCountFrequency (%)
26.9 1
0.5%
26.3 1
0.5%
25.4 1
0.5%
24.5 1
0.5%
24 1
0.5%
22.3 1
0.5%
21.6 2
1.0%
20.9 2
1.0%
20.4 1
0.5%
20.3 1
0.5%

Classes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1.0
114 
0.0
88 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters606
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 114
56.4%
0.0 88
43.6%

Length

2023-09-16T18:18:59.126855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T18:18:59.311951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 114
56.4%
0.0 88
43.6%

Most occurring characters

ValueCountFrequency (%)
0 290
47.9%
. 202
33.3%
1 114
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 404
66.7%
Other Punctuation 202
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290
71.8%
1 114
 
28.2%
Other Punctuation
ValueCountFrequency (%)
. 202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 606
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290
47.9%
. 202
33.3%
1 114
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290
47.9%
. 202
33.3%
1 114
 
18.8%

Interactions

2023-09-16T18:18:50.970526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:33.525048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:35.706702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:37.727242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:39.403455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:41.063861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:42.675958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:44.257848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:46.065588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:47.747156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:49.360298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:51.118999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:33.686551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:35.972936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:37.872725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:39.555120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:41.204969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:42.809309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:44.406606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:46.234850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:47.878076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:49.508852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:51.255193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:33.856574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:36.146960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:38.023143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:39.706425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:41.361078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:42.958557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:44.536500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:46.381301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:48.041444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:49.658875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:51.418212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:34.040259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:36.331939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:38.187858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:39.867164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:41.513231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:43.108104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:44.703489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:46.531037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:48.176594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:49.822632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:51.568403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:34.255832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:36.655852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:38.342953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:40.031218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:41.662129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:43.261488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:45.075117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:46.699128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:48.342443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:49.970253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:51.716428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:34.446469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:36.829869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:38.486598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:40.182616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:41.796046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:43.413358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:45.241973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:46.831049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:48.497037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:50.119847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:51.849341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:34.601890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:36.979665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:38.639552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:40.331558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:41.946283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:43.541648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:45.368462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:46.992781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:48.643825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:50.256825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:51.982326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:34.771638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:37.124006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:38.788786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:40.485051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:42.093368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:43.673751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:45.503137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:47.129239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:48.777603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:50.404791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:52.130924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:34.938520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:37.290914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:38.953033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:40.634645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:42.246261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:43.826759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:45.650963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:47.299408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:48.927160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:50.553932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:52.285414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:35.141803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:37.448902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:39.103690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:40.784256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:42.410599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:43.974365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:45.801113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:47.449125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:49.094577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:50.700194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:52.415422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:35.456329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:37.573583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:39.255259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:40.931871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:42.543085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:44.106306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:45.942909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:47.595599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:49.235472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-16T18:18:50.836495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-16T18:18:59.436944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0dayRHWsRainFFMCDMCDCISIBUIFWImonthClasses
Unnamed: 01.0000.119-0.317-0.131-0.0430.2450.2580.0560.2230.1870.2240.8080.365
day0.1191.000-0.0790.062-0.1030.2240.4850.4580.2160.5030.3270.0000.169
RH-0.317-0.0791.0000.1710.132-0.623-0.453-0.288-0.601-0.413-0.5580.1810.364
Ws-0.1310.0620.1711.0000.022-0.061-0.0100.0650.0340.0190.0280.1120.000
Rain-0.043-0.1030.1320.0221.000-0.732-0.516-0.561-0.728-0.529-0.6950.0450.483
FFMC0.2450.224-0.623-0.061-0.7321.0000.7970.6970.9910.7780.9650.2190.868
DMC0.2580.485-0.453-0.010-0.5160.7971.0000.8770.7980.9860.9060.3050.648
DC0.0560.458-0.2880.065-0.5610.6970.8771.0000.7100.9350.8250.2800.568
ISI0.2230.216-0.6010.034-0.7280.9910.7980.7101.0000.7820.9730.2360.873
BUI0.1870.503-0.4130.019-0.5290.7780.9860.9350.7821.0000.8980.3060.678
FWI0.2240.327-0.5580.028-0.6950.9650.9060.8250.9730.8981.0000.2450.867
month0.8080.0000.1810.1120.0450.2190.3050.2800.2360.3060.2451.0000.301
Classes0.3650.1690.3640.0000.4830.8680.6480.5680.8730.6780.8670.3011.000

Missing values

2023-09-16T18:18:52.646898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T18:18:52.951703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0daymonthyearRHWsRainFFMCDMCDCISIBUIFWIClasses
016411.07.02012.056.015.02.974.87.19.51.66.80.80.0
110413.09.02012.086.021.04.640.91.37.50.11.80.00.0
28424.08.02012.064.014.00.088.940.5171.39.050.920.91.0
321228.08.02012.056.014.00.479.237.0166.02.130.66.10.0
41862.08.02012.034.014.00.093.310.821.413.810.613.51.0
524025.09.02012.070.015.00.079.913.836.12.414.13.00.0
611322.09.02012.050.019.00.677.810.641.42.412.92.80.0
71296.06.02012.054.011.00.183.78.426.33.19.33.11.0
87717.08.02012.052.018.00.089.316.0100.79.722.914.61.0
91819.06.02012.055.016.00.179.94.516.02.55.31.40.0
Unnamed: 0daymonthyearRHWsRainFFMCDMCDCISIBUIFWIClasses
1921329.06.02012.059.018.00.178.18.514.72.48.31.90.0
19324530.09.02012.064.015.00.267.33.816.51.24.80.50.0
1942223.06.02012.062.018.00.181.48.247.73.311.53.81.0
19520420.08.02012.081.015.00.083.734.4107.03.838.19.01.0
1967313.08.02012.063.015.00.087.019.085.15.924.410.21.0
19711524.09.02012.065.019.00.668.35.515.21.55.80.70.0
1981617.06.02012.089.016.00.637.31.17.80.01.60.00.0
1991009.09.02012.077.015.01.056.12.18.40.72.60.20.0
20019511.08.02012.031.015.00.094.222.546.316.622.421.61.0
20111120.09.02012.084.018.00.083.813.549.34.516.06.31.0